Learner Modeling Based On Bayesian Networks
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| Author | : Richard E. Neapolitan |
| Publisher | : Prentice Hall |
| Total Pages | : 704 |
| Release | : 2004 |
| Genre | : Computers |
| ISBN | : |
In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.
| Author | : Anouar Tadlaoui Mouenis |
| Publisher | : |
| Total Pages | : |
| Release | : 2015 |
| Genre | : Computers |
| ISBN | : |
The work presented in this chapter lies within Learner modeling in an adaptive educational system construed as a computational modeling of the learner. All actions of the learner in a learning situation on an adaptive hypermedia systems are not limited to valid or invalid actions (true and false), but they are a set of actions that characterize the learning path of his formation. Thus, we cannot represent the information from the system of each learner using relative data. It requires putting our work in a probabilistic context due to the changes in the learner model information during formation. We propose in this work to use Bayesian networks as a probabilistic framework to resolve the issue of dynamic management and update of the learner model. The experiments and results presented in this work are arguments in favor of our hypothesis, and can also promote reusing the modeling obtained through different systems and similar modeling situations.
| Author | : Adnan Darwiche |
| Publisher | : Cambridge University Press |
| Total Pages | : 561 |
| Release | : 2009-04-06 |
| Genre | : Computers |
| ISBN | : 0521884381 |
This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.
| Author | : Tadlaoui, Mouenis Anouar |
| Publisher | : IGI Global |
| Total Pages | : 216 |
| Release | : 2018-11-16 |
| Genre | : Education |
| ISBN | : 152257414X |
Teachers use e-learning systems to develop course notes and web-based activities to communicate with learners on one side and monitor and classify their progress on the other. Learners use it for learning, communication, and collaboration. Adaptive e-learning systems often employ learner models, and the behavior of an adaptive system varies depending on the data from the learner model and the learner's profile. Without knowing anything about the learner who uses the system, a system would behave in exactly the same way for all learners. Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems: Emerging Research and Opportunities is a collection of research on the use of Bayesian networks and methods as a probabilistic formalism for the management of the learner model in adaptive hypermedia. It specifically discusses comparative studies, transformation rules, and case diagrams that support all phases of the learner model and the use of Bayesian networks and multi-entity Bayesian networks to manage dynamic aspects of this model. While highlighting topics such as developing the learner model, learning management systems, and modeling techniques, this book is ideally designed for instructional designers, course administrators, educators, researchers, and professionals.
| Author | : Lytras, Miltiadis D. |
| Publisher | : IGI Global |
| Total Pages | : 373 |
| Release | : 2019-05-31 |
| Genre | : Education |
| ISBN | : 1522590323 |
Various technologies and applications such as cognitive computing, artificial intelligence, and learning analytics have received increased attention in recent years. The growing demand behind their adoption and exploitation in different application contexts has captured the attention of learning technology specialists, computer engineers, and business researchers who are attempting to decipher the phenomenon of personalized e-learning, its relation to already conducted research, and its implications for new research opportunities that effect innovations in teaching. Cognitive Computing in Technology-Enhanced Learning is a critical resource publication that aims to demonstrate state-of-the-art approaches of advanced data mining systems in e-learning, such as MOOCs and other innovative technologies, to improve learning analytics, as well as to show how new and advanced user interaction designs, educational models, and adoptive strategies can expand sustainability in applied learning technologies. Highlighting a range of topics such as augmented reality, ethics, and online learning environments, this book is ideal for educators, instructional designers, higher education faculty, school administrators, academicians, researchers, and students.
| Author | : Marco Scutari |
| Publisher | : CRC Press |
| Total Pages | : 275 |
| Release | : 2021-07-28 |
| Genre | : Computers |
| ISBN | : 1000410382 |
Explains the material step-by-step starting from meaningful examples Steps detailed with R code in the spirit of reproducible research Real world data analyses from a Science paper reproduced and explained in detail Examples span a variety of fields across social and life sciences Overview of available software in and outside R
| Author | : Adnan Darwiche |
| Publisher | : Cambridge University Press |
| Total Pages | : 549 |
| Release | : 2009-04-06 |
| Genre | : Computers |
| ISBN | : 1139478907 |
This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.
| Author | : Radford M. Neal |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 194 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1461207452 |
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.
| Author | : Russell G. Almond |
| Publisher | : Springer |
| Total Pages | : 678 |
| Release | : 2015-03-10 |
| Genre | : Social Science |
| ISBN | : 1493921258 |
Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as an integral component of a principled design process, and illustrates the ideas with an in-depth look at the BioMass project: An interactive, standards-based, web-delivered demonstration assessment of science inquiry in genetics. This book is both a resource for professionals interested in assessment and advanced students. Its clear exposition, worked-through numerical examples, and demonstrations from real and didactic applications provide invaluable illustrations of how to use Bayes nets in educational assessment. Exercises follow each chapter, and the online companion site provides a glossary, data sets and problem setups, and links to computational resources.
| Author | : M.I. Jordan |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 658 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 9401150141 |
In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of different points of view. There has been substantial progress in these different communities and surprising convergence has developed between the formalisms. The awareness of this convergence and the growing interest of researchers in understanding the essential unity of the subject underlies the current volume. Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail. Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists.
